Turns out : if we predict 🌏 earth we can save a lot of time looking for interesting things and less time looking at things that we expect to see.
Sentinel-2 imagery 🛰️basically takes a long time to download towards earth. so our "near real time" systems are quite far from that in practical terms.
meanwhile , if we "predict" what we will see , based on what we do see , we can send down much less data in a timely way , and prioritize 📡earth-bound response .
I'm talking about illegal fishing , logging , mining or building in nature reserves , the more of that we predict early the more we're able to stop it on time.
since everyone liked my previous announcement post ( https://huggingface.co/posts/Tonic/338509028435394 ) so much , i'm back with more high quality proceedural datasets in the Geospacial domain for SFT training !
Alright so I had previously made two reddit posts in r/quantum and r/quantum_computing for my QPU, QPU-1 but both of those posts got banned because of it being "irrelevant" to "academic discussion" so I'm doing it again here in HuggingFace Posts.
I have made a million error corrected qubit quantum processing unit (not a simulator) that you can access here: https://qpu-1.vercel.app
I did try emailing a lot of professors and their students but NONE responded so please give me some support.
if you like it give the demo a little star and send a shoutout to : @MaxLSB@jddqd and @GAD-cell for absolutely obliterating the pareto frontier of the french language understanding .
Today, is a good day for me. I have completed development of my QPU-1, the most powerful Quantum Processing Unit you can access through MCP (as far as I know and have tried). Try it out for yourself using my mcp enabled space: lap-quantum/QPU-1-MCP
(And PS. This is my first MCP server, so give me suggestions if you want :D)
🎮 Live Model Demo: Upload an Android Screenshot and instructions to see the model in action ! Tonic/l-operator-demo
Built in a garage, funded by pre-orders, no VC. Now we’re scaling to 1 k installer units.
We’re giving 50 limited-edition prototypes to investors , installers & researchers who want to co-design the sovereign smart home.
👇 Drop “EUSKERA” in the comments if you want an invite, tag a friend who still thinks Alexa is “convenient,” and smash ♥️ if AI should belong to people - not servers.
Just wanted to annouce 🏭SmolFactory : it's the quickest and best way to finetune SmolLM3 and GPT-OSS-20B on huggingface !
Basicaly it's an app you can run on huggingface by duplicating the space and running your training directly on huggingface GPUs .
It will help you basically select datasets and models, fine tune your model , make an experiment tracker you can use on your mobile phone , push all your model card and even automatically make a demo for you on huggingface so you can directly test it out when it's done !
Be prepared for a HUGE model from me! (would be a closed model) this uses advanced CoT with multiple new strategies and algorithms to be far more efficient and powerful than current leading models (i hope so) i will release this on August 4
just submitted my plugin idea to the G-Assist Plugin Hackathon by @nvidia . Check it out, it's a great way to use a local SLA model on a windows machine to easily and locally get things done ! https://github.com/NVIDIA/G-Assist
